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<div class="title">bintree/cuda/bintree_gen_inline.h</div>  </div>
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<div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">/*</span>
<a name="l00002"></a>00002 <span class="comment"> * Copyright (c) 2010-2011, NVIDIA Corporation</span>
<a name="l00003"></a>00003 <span class="comment"> * All rights reserved.</span>
<a name="l00004"></a>00004 <span class="comment"> *</span>
<a name="l00005"></a>00005 <span class="comment"> * Redistribution and use in source and binary forms, with or without</span>
<a name="l00006"></a>00006 <span class="comment"> * modification, are permitted provided that the following conditions are met:</span>
<a name="l00007"></a>00007 <span class="comment"> *   * Redistributions of source code must retain the above copyright</span>
<a name="l00008"></a>00008 <span class="comment"> *     notice, this list of conditions and the following disclaimer.</span>
<a name="l00009"></a>00009 <span class="comment"> *   * Redistributions in binary form must reproduce the above copyright</span>
<a name="l00010"></a>00010 <span class="comment"> *     notice, this list of conditions and the following disclaimer in the</span>
<a name="l00011"></a>00011 <span class="comment"> *     documentation and/or other materials provided with the distribution.</span>
<a name="l00012"></a>00012 <span class="comment"> *   * Neither the name of NVIDIA Corporation nor the</span>
<a name="l00013"></a>00013 <span class="comment"> *     names of its contributors may be used to endorse or promote products</span>
<a name="l00014"></a>00014 <span class="comment"> *     derived from this software without specific prior written permission.</span>
<a name="l00015"></a>00015 <span class="comment"> *</span>
<a name="l00016"></a>00016 <span class="comment"> * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS &quot;AS IS&quot; AND</span>
<a name="l00017"></a>00017 <span class="comment"> * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED</span>
<a name="l00018"></a>00018 <span class="comment"> * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE</span>
<a name="l00019"></a>00019 <span class="comment"> * DISCLAIMED. IN NO EVENT SHALL &lt;COPYRIGHT HOLDER&gt; BE LIABLE FOR ANY</span>
<a name="l00020"></a>00020 <span class="comment"> * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES</span>
<a name="l00021"></a>00021 <span class="comment"> * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;</span>
<a name="l00022"></a>00022 <span class="comment"> * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND</span>
<a name="l00023"></a>00023 <span class="comment"> * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT</span>
<a name="l00024"></a>00024 <span class="comment"> * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS</span>
<a name="l00025"></a>00025 <span class="comment"> * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span>
<a name="l00026"></a>00026 <span class="comment"> */</span>
<a name="l00027"></a>00027 
<a name="l00028"></a>00028 <span class="preprocessor">#include &lt;<a class="code" href="functors_8h.html" title="Defines some general purpose functors.">nih/basic/functors.h</a>&gt;</span>
<a name="l00029"></a>00029 <span class="preprocessor">#include &lt;<a class="code" href="algorithms_8h.html" title="Defines some general purpose algorithms.">nih/basic/algorithms.h</a>&gt;</span>
<a name="l00030"></a>00030 <span class="preprocessor">#include &lt;<a class="code" href="scan_8h.html" title="Define CUDA based scan primitives.">nih/basic/cuda/scan.h</a>&gt;</span>
<a name="l00031"></a>00031 <span class="preprocessor">#include &lt;nih/basic/utils.h&gt;</span>
<a name="l00032"></a>00032 
<a name="l00033"></a>00033 <span class="keyword">namespace </span>nih {
<a name="l00034"></a>00034 <span class="keyword">namespace </span>cuda {
<a name="l00035"></a>00035 <span class="keyword">namespace </span>bintree {
<a name="l00036"></a>00036 
<a name="l00037"></a>00037 <span class="keyword">typedef</span> Bintree_gen_context::Split_task Split_task;
<a name="l00038"></a>00038 
<a name="l00039"></a>00039 <span class="comment">// find the most significant bit smaller than start by which code0 and code1 differ</span>
<a name="l00040"></a>00040 <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Integer&gt;
<a name="l00041"></a>00041 FORCE_INLINE NIH_HOST_DEVICE int32 find_leading_bit_difference(
<a name="l00042"></a>00042     <span class="keyword">const</span>  int32  start_level,
<a name="l00043"></a>00043     <span class="keyword">const</span> Integer code0,
<a name="l00044"></a>00044     <span class="keyword">const</span> Integer code1)
<a name="l00045"></a>00045 {
<a name="l00046"></a>00046     int32 level = start_level;
<a name="l00047"></a>00047 
<a name="l00048"></a>00048     <span class="keywordflow">while</span> (level &gt;= 0)
<a name="l00049"></a>00049     {
<a name="l00050"></a>00050         <span class="keyword">const</span> Integer mask = Integer(1u) &lt;&lt; level;
<a name="l00051"></a>00051 
<a name="l00052"></a>00052         <span class="keywordflow">if</span> ((code0 &amp; mask) !=
<a name="l00053"></a>00053             (code1 &amp; mask))
<a name="l00054"></a>00054             <span class="keywordflow">break</span>;
<a name="l00055"></a>00055 
<a name="l00056"></a>00056         --level;
<a name="l00057"></a>00057     }
<a name="l00058"></a>00058     <span class="keywordflow">return</span> level;
<a name="l00059"></a>00059 }
<a name="l00060"></a>00060 
<a name="l00061"></a>00061 <span class="comment">// do a single kd-split for all nodes in the input task queue, and generate</span>
<a name="l00062"></a>00062 <span class="comment">// a corresponding list of output tasks</span>
<a name="l00063"></a>00063 <span class="keyword">template</span> &lt;u<span class="keywordtype">int</span>32 BLOCK_SIZE, <span class="keyword">typename</span> Tree, <span class="keyword">typename</span> Integer&gt;
<a name="l00064"></a>00064 __global__ <span class="keywordtype">void</span> split_kernel(
<a name="l00065"></a>00065     Tree                tree,
<a name="l00066"></a>00066     <span class="keyword">const</span> uint32        max_leaf_size,
<a name="l00067"></a>00067     <span class="keyword">const</span> <span class="keywordtype">bool</span>          keep_singletons,
<a name="l00068"></a>00068     <span class="keyword">const</span> uint32        grid_size,
<a name="l00069"></a>00069     <span class="keyword">const</span> Integer*      codes,
<a name="l00070"></a>00070     <span class="keyword">const</span> uint32        in_tasks_count,
<a name="l00071"></a>00071     <span class="keyword">const</span> Split_task*   in_tasks,
<a name="l00072"></a>00072     <span class="keyword">const</span> uint32*       in_skip_nodes,
<a name="l00073"></a>00073     uint32*             out_tasks_count,
<a name="l00074"></a>00074     Split_task*         out_tasks,
<a name="l00075"></a>00075     uint32*             out_skip_nodes,
<a name="l00076"></a>00076     <span class="keyword">const</span> uint32        out_nodes_count,
<a name="l00077"></a>00077     uint32*             out_leaf_count)
<a name="l00078"></a>00078 {
<a name="l00079"></a>00079     <span class="keyword">const</span> uint32 LOG_WARP_SIZE = 5;
<a name="l00080"></a>00080     <span class="keyword">const</span> uint32 WARP_SIZE = 1u &lt;&lt; LOG_WARP_SIZE;
<a name="l00081"></a>00081 
<a name="l00082"></a>00082     <span class="keyword">volatile</span> __shared__ uint32 warp_offset[ BLOCK_SIZE &gt;&gt; LOG_WARP_SIZE ];
<a name="l00083"></a>00083 
<a name="l00084"></a>00084     <span class="keyword">const</span> uint32 warp_tid = threadIdx.x &amp; (WARP_SIZE-1);
<a name="l00085"></a>00085     <span class="keyword">const</span> uint32 warp_id  = threadIdx.x &gt;&gt; LOG_WARP_SIZE;
<a name="l00086"></a>00086 
<a name="l00087"></a>00087     <span class="keyword">volatile</span> __shared__ uint32 sm_red[ BLOCK_SIZE * 2 ];
<a name="l00088"></a>00088     <span class="keyword">volatile</span> uint32* warp_red = sm_red + WARP_SIZE * 2 * warp_id;
<a name="l00089"></a>00089 
<a name="l00090"></a>00090     <span class="comment">// loop through all logical blocks associated to this physical one</span>
<a name="l00091"></a>00091     <span class="keywordflow">for</span> (uint32 base_idx = blockIdx.x * BLOCK_SIZE;
<a name="l00092"></a>00092                 base_idx &lt; in_tasks_count;
<a name="l00093"></a>00093                 base_idx += grid_size)
<a name="l00094"></a>00094     {
<a name="l00095"></a>00095         uint32 output_count = 0;
<a name="l00096"></a>00096         uint32 split_index;
<a name="l00097"></a>00097 
<a name="l00098"></a>00098         <span class="keyword">const</span> uint32 task_id = threadIdx.x + base_idx;
<a name="l00099"></a>00099 
<a name="l00100"></a>00100         uint32 node;
<a name="l00101"></a>00101         uint32 begin;
<a name="l00102"></a>00102         uint32 end;
<a name="l00103"></a>00103         uint32 level;
<a name="l00104"></a>00104         uint32 skip_node;
<a name="l00105"></a>00105 
<a name="l00106"></a>00106         <span class="comment">// check if the task id is in range, and if so try to find its split plane</span>
<a name="l00107"></a>00107         <span class="keywordflow">if</span> (task_id &lt; in_tasks_count)
<a name="l00108"></a>00108         {
<a name="l00109"></a>00109             <span class="keyword">const</span> Split_task in_task = in_tasks[ task_id ];
<a name="l00110"></a>00110 
<a name="l00111"></a>00111             node  = in_task.m_node;
<a name="l00112"></a>00112             begin = in_task.m_begin;
<a name="l00113"></a>00113             end   = in_task.m_end;
<a name="l00114"></a>00114             level = in_task.m_input;
<a name="l00115"></a>00115 
<a name="l00116"></a>00116             skip_node = in_skip_nodes[ task_id ];
<a name="l00117"></a>00117 
<a name="l00118"></a>00118             <span class="keywordflow">if</span> (!keep_singletons)
<a name="l00119"></a>00119             {
<a name="l00120"></a>00120                 level = find_leading_bit_difference(
<a name="l00121"></a>00121                     level,
<a name="l00122"></a>00122                     codes[begin],
<a name="l00123"></a>00123                     codes[end-1] );
<a name="l00124"></a>00124             }
<a name="l00125"></a>00125 
<a name="l00126"></a>00126             <span class="comment">// check whether the input node really needs to be split</span>
<a name="l00127"></a>00127             <span class="keywordflow">if</span> (end - begin &gt; max_leaf_size &amp;&amp; level != uint32(-1))
<a name="l00128"></a>00128             {
<a name="l00129"></a>00129                 <span class="comment">// find the &quot;partitioning pivot&quot; using a binary search</span>
<a name="l00130"></a>00130                 split_index = find_pivot(
<a name="l00131"></a>00131                     codes + begin,
<a name="l00132"></a>00132                     end - begin,
<a name="l00133"></a>00133                     mask_and&lt;Integer&gt;( Integer(1u) &lt;&lt; level ) ) - codes;
<a name="l00134"></a>00134 
<a name="l00135"></a>00135                 output_count = (split_index == begin || split_index == end) ? 1u : 2u;
<a name="l00136"></a>00136             }
<a name="l00137"></a>00137         }
<a name="l00138"></a>00138 
<a name="l00139"></a>00139         <span class="keyword">const</span> uint32 task_offset = cuda::alloc( output_count, out_tasks_count, warp_tid, warp_red, warp_offset + warp_id );
<a name="l00140"></a>00140         <span class="keyword">const</span> uint32 node_offset = out_nodes_count + task_offset;
<a name="l00141"></a>00141         <span class="keyword">const</span> uint32 first_end   = (output_count == 1) ? end       : split_index;
<a name="l00142"></a>00142         <span class="keyword">const</span> uint32 first_skip  = (output_count == 1) ? skip_node : node_offset+1;
<a name="l00143"></a>00143 
<a name="l00144"></a>00144         <span class="keywordflow">if</span> (output_count &gt;= 1) { out_tasks[ task_offset+0 ] = Split_task( node_offset+0, begin, first_end, level-1 ); out_skip_nodes[ task_offset+0 ] = first_skip; }
<a name="l00145"></a>00145         <span class="keywordflow">if</span> (output_count == 2) { out_tasks[ task_offset+1 ] = Split_task( node_offset+1, split_index, end, level-1 ); out_skip_nodes[ task_offset+1 ] = skip_node; }
<a name="l00146"></a>00146 
<a name="l00147"></a>00147         <span class="keyword">const</span> <span class="keywordtype">bool</span> generate_leaf = (output_count == 0 &amp;&amp; task_id &lt; in_tasks_count);
<a name="l00148"></a>00148 
<a name="l00149"></a>00149         <span class="comment">// count how many leaves we need to generate</span>
<a name="l00150"></a>00150         <span class="keyword">const</span> uint32 leaf_index = cuda::alloc&lt;1&gt;( generate_leaf, out_leaf_count, warp_tid, warp_offset + warp_id );
<a name="l00151"></a>00151 
<a name="l00152"></a>00152         <span class="comment">// write the parent node</span>
<a name="l00153"></a>00153         <span class="keywordflow">if</span> (task_id &lt; in_tasks_count)
<a name="l00154"></a>00154         {
<a name="l00155"></a>00155             tree.write_node(
<a name="l00156"></a>00156                 node,
<a name="l00157"></a>00157                 output_count ? split_index != begin : <span class="keyword">false</span>,
<a name="l00158"></a>00158                 output_count ? split_index != end   : <span class="keyword">false</span>,
<a name="l00159"></a>00159                 output_count ? node_offset          : leaf_index,
<a name="l00160"></a>00160                 skip_node,
<a name="l00161"></a>00161                 level,
<a name="l00162"></a>00162                 begin,
<a name="l00163"></a>00163                 end,
<a name="l00164"></a>00164                 output_count ? split_index : uint32(-1) );
<a name="l00165"></a>00165 
<a name="l00166"></a>00166             <span class="comment">// make a leaf if necessary</span>
<a name="l00167"></a>00167             <span class="keywordflow">if</span> (output_count == 0)
<a name="l00168"></a>00168                 tree.write_leaf( leaf_index, begin, end );
<a name="l00169"></a>00169         }
<a name="l00170"></a>00170     }
<a name="l00171"></a>00171 }
<a name="l00172"></a>00172 <span class="comment">// generate a leaf for each task</span>
<a name="l00173"></a>00173 <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tree, u<span class="keywordtype">int</span>32 BLOCK_SIZE&gt;
<a name="l00174"></a>00174 __global__ <span class="keywordtype">void</span> gen_leaves_kernel(
<a name="l00175"></a>00175     Tree                tree,
<a name="l00176"></a>00176     <span class="keyword">const</span> uint32        grid_size,
<a name="l00177"></a>00177     <span class="keyword">const</span> uint32        in_tasks_count,
<a name="l00178"></a>00178     <span class="keyword">const</span> Split_task*   in_tasks,
<a name="l00179"></a>00179     <span class="keyword">const</span> uint32*       in_skip_nodes,
<a name="l00180"></a>00180     uint32*             out_leaf_count)
<a name="l00181"></a>00181 {
<a name="l00182"></a>00182     <span class="keyword">const</span> uint32 LOG_WARP_SIZE = 5;
<a name="l00183"></a>00183     <span class="keyword">const</span> uint32 WARP_SIZE = 1u &lt;&lt; LOG_WARP_SIZE;
<a name="l00184"></a>00184 
<a name="l00185"></a>00185     __shared__ uint32 warp_offset[ BLOCK_SIZE &gt;&gt; LOG_WARP_SIZE ];
<a name="l00186"></a>00186 
<a name="l00187"></a>00187     <span class="keyword">const</span> uint32 warp_tid = threadIdx.x &amp; (WARP_SIZE-1);
<a name="l00188"></a>00188     <span class="keyword">const</span> uint32 warp_id  = threadIdx.x &gt;&gt; LOG_WARP_SIZE;
<a name="l00189"></a>00189 
<a name="l00190"></a>00190     <span class="comment">// loop through all logical blocks associated to this physical one</span>
<a name="l00191"></a>00191     <span class="keywordflow">for</span> (uint32 base_idx = blockIdx.x * BLOCK_SIZE;
<a name="l00192"></a>00192                 base_idx &lt; in_tasks_count;
<a name="l00193"></a>00193                 base_idx += grid_size)
<a name="l00194"></a>00194     {
<a name="l00195"></a>00195         <span class="keyword">const</span> uint32 task_id = threadIdx.x + base_idx;
<a name="l00196"></a>00196 
<a name="l00197"></a>00197         uint32 node;
<a name="l00198"></a>00198         uint32 begin;
<a name="l00199"></a>00199         uint32 end;
<a name="l00200"></a>00200         uint32 level;
<a name="l00201"></a>00201         uint32 skip_node;
<a name="l00202"></a>00202 
<a name="l00203"></a>00203         <span class="comment">// check if the task id is in range, and if so try to find its split plane</span>
<a name="l00204"></a>00204         <span class="keywordflow">if</span> (task_id &lt; in_tasks_count)
<a name="l00205"></a>00205         {
<a name="l00206"></a>00206             <span class="keyword">const</span> Split_task in_task = in_tasks[ task_id ];
<a name="l00207"></a>00207 
<a name="l00208"></a>00208             node  = in_task.m_node;
<a name="l00209"></a>00209             begin = in_task.m_begin;
<a name="l00210"></a>00210             end   = in_task.m_end;
<a name="l00211"></a>00211             level = in_task.m_input;
<a name="l00212"></a>00212             skip_node = in_skip_nodes[ task_id ];
<a name="l00213"></a>00213         }
<a name="l00214"></a>00214 
<a name="l00215"></a>00215         <span class="comment">// alloc output slots</span>
<a name="l00216"></a>00216         uint32 leaf_index = cuda::alloc&lt;1&gt;( task_id &lt; in_tasks_count, out_leaf_count, warp_tid, warp_offset + warp_id );
<a name="l00217"></a>00217 
<a name="l00218"></a>00218         <span class="comment">// write the parent node</span>
<a name="l00219"></a>00219         <span class="keywordflow">if</span> (task_id &lt; in_tasks_count)
<a name="l00220"></a>00220         {
<a name="l00221"></a>00221             tree.write_node( node, <span class="keyword">false</span>, <span class="keyword">false</span>, leaf_index, skip_node, level, begin, end, uint32(-1) );
<a name="l00222"></a>00222             tree.write_leaf( leaf_index, begin, end );
<a name="l00223"></a>00223         }
<a name="l00224"></a>00224     }
<a name="l00225"></a>00225 }
<a name="l00226"></a>00226 
<a name="l00227"></a>00227 <span class="comment">// do a single kd-split for all nodes in the input task queue, and generate</span>
<a name="l00228"></a>00228 <span class="comment">// a corresponding list of output tasks</span>
<a name="l00229"></a>00229 <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tree, <span class="keyword">typename</span> Integer&gt;
<a name="l00230"></a>00230 <span class="keywordtype">void</span> split(
<a name="l00231"></a>00231     Tree                tree,
<a name="l00232"></a>00232     <span class="keyword">const</span> uint32        max_leaf_size,
<a name="l00233"></a>00233     <span class="keyword">const</span> <span class="keywordtype">bool</span>          keep_singletons,
<a name="l00234"></a>00234     <span class="keyword">const</span> Integer*      codes,
<a name="l00235"></a>00235     <span class="keyword">const</span> uint32        in_tasks_count,
<a name="l00236"></a>00236     <span class="keyword">const</span> Split_task*   in_tasks,
<a name="l00237"></a>00237     <span class="keyword">const</span> uint32*       in_skip_nodes,
<a name="l00238"></a>00238     uint32*             out_tasks_count,
<a name="l00239"></a>00239     Split_task*         out_tasks,
<a name="l00240"></a>00240     uint32*             out_skip_nodes,
<a name="l00241"></a>00241     <span class="keyword">const</span> uint32        out_nodes_count,
<a name="l00242"></a>00242     uint32*             out_leaf_count)
<a name="l00243"></a>00243 {
<a name="l00244"></a>00244     <span class="keyword">const</span> uint32 BLOCK_SIZE = 128;
<a name="l00245"></a>00245     <span class="keyword">const</span> <span class="keywordtype">size_t</span> max_blocks = thrust::detail::backend::cuda::arch::max_active_blocks(split_kernel&lt;BLOCK_SIZE,Tree,Integer&gt;, BLOCK_SIZE, 0);
<a name="l00246"></a>00246     <span class="keyword">const</span> <span class="keywordtype">size_t</span> n_blocks   = <a class="code" href="group__vectors.html#ga5809911d15c32de18955c59865379695">nih::min</a>( max_blocks, (in_tasks_count + BLOCK_SIZE-1) / BLOCK_SIZE );
<a name="l00247"></a>00247     <span class="keyword">const</span> <span class="keywordtype">size_t</span> grid_size  = n_blocks * BLOCK_SIZE;
<a name="l00248"></a>00248 
<a name="l00249"></a>00249     split_kernel&lt;BLOCK_SIZE&gt; &lt;&lt;&lt;n_blocks,BLOCK_SIZE&gt;&gt;&gt; (
<a name="l00250"></a>00250         tree,
<a name="l00251"></a>00251         max_leaf_size,
<a name="l00252"></a>00252         keep_singletons,
<a name="l00253"></a>00253         grid_size,
<a name="l00254"></a>00254         codes,
<a name="l00255"></a>00255         in_tasks_count,
<a name="l00256"></a>00256         in_tasks,
<a name="l00257"></a>00257         in_skip_nodes,
<a name="l00258"></a>00258         out_tasks_count,
<a name="l00259"></a>00259         out_tasks,
<a name="l00260"></a>00260         out_skip_nodes,
<a name="l00261"></a>00261         out_nodes_count,
<a name="l00262"></a>00262         out_leaf_count );
<a name="l00263"></a>00263 
<a name="l00264"></a>00264     cudaThreadSynchronize();
<a name="l00265"></a>00265 }
<a name="l00266"></a>00266 
<a name="l00267"></a>00267 <span class="comment">// generate a leaf for each task</span>
<a name="l00268"></a>00268 <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tree&gt;
<a name="l00269"></a>00269 <span class="keywordtype">void</span> gen_leaves(
<a name="l00270"></a>00270     Tree                tree,
<a name="l00271"></a>00271     <span class="keyword">const</span> uint32        in_tasks_count,
<a name="l00272"></a>00272     <span class="keyword">const</span> Split_task*   in_tasks,
<a name="l00273"></a>00273     <span class="keyword">const</span> uint32*       in_skip_nodes,
<a name="l00274"></a>00274     uint32*             out_leaf_count)
<a name="l00275"></a>00275 {
<a name="l00276"></a>00276     <span class="keyword">const</span> uint32 BLOCK_SIZE = 128;
<a name="l00277"></a>00277     <span class="keyword">const</span> <span class="keywordtype">size_t</span> max_blocks = thrust::detail::backend::cuda::arch::max_active_blocks(gen_leaves_kernel&lt;Tree,BLOCK_SIZE&gt;, BLOCK_SIZE, 0);
<a name="l00278"></a>00278     <span class="keyword">const</span> <span class="keywordtype">size_t</span> n_blocks   = <a class="code" href="group__vectors.html#ga5809911d15c32de18955c59865379695">nih::min</a>( max_blocks, (in_tasks_count + BLOCK_SIZE-1) / BLOCK_SIZE );
<a name="l00279"></a>00279     <span class="keyword">const</span> <span class="keywordtype">size_t</span> grid_size  = n_blocks * BLOCK_SIZE;
<a name="l00280"></a>00280 
<a name="l00281"></a>00281     gen_leaves_kernel&lt;Tree,BLOCK_SIZE&gt; &lt;&lt;&lt;n_blocks,BLOCK_SIZE&gt;&gt;&gt; (
<a name="l00282"></a>00282         tree,
<a name="l00283"></a>00283         grid_size,
<a name="l00284"></a>00284         in_tasks_count,
<a name="l00285"></a>00285         in_tasks,
<a name="l00286"></a>00286         in_skip_nodes,
<a name="l00287"></a>00287         out_leaf_count );
<a name="l00288"></a>00288 
<a name="l00289"></a>00289     cudaThreadSynchronize();
<a name="l00290"></a>00290 }
<a name="l00291"></a>00291 
<a name="l00292"></a>00292 } <span class="comment">// namespace bintree</span>
<a name="l00293"></a>00293 
<a name="l00294"></a>00294 <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tree, <span class="keyword">typename</span> Integer&gt;
<a name="l00295"></a><a class="code" href="group__bintree.html#gad76a50ae08ab4d525f748a7cbcc0fb6e">00295</a> <span class="keywordtype">void</span> <a class="code" href="group__bintree.html#gad76a50ae08ab4d525f748a7cbcc0fb6e">generate</a>(
<a name="l00296"></a>00296     <a class="code" href="structnih_1_1cuda_1_1_bintree__gen__context.html">Bintree_gen_context</a>&amp; context,
<a name="l00297"></a>00297     <span class="keyword">const</span> uint32    n_codes,
<a name="l00298"></a>00298     <span class="keyword">const</span> Integer*  codes,
<a name="l00299"></a>00299     <span class="keyword">const</span> uint32    bits,
<a name="l00300"></a>00300     <span class="keyword">const</span> uint32    max_leaf_size,
<a name="l00301"></a>00301     <span class="keyword">const</span> <span class="keywordtype">bool</span>      keep_singletons,
<a name="l00302"></a>00302     Tree&amp;           tree)
<a name="l00303"></a>00303 {
<a name="l00304"></a>00304     tree.reserve_nodes( n_codes * 2 );
<a name="l00305"></a>00305     tree.reserve_leaves( n_codes );
<a name="l00306"></a>00306 
<a name="l00307"></a>00307     <span class="comment">// start building the octree</span>
<a name="l00308"></a>00308     need_space( context.m_task_queues[0], n_codes );
<a name="l00309"></a>00309     need_space( context.m_task_queues[1], n_codes );
<a name="l00310"></a>00310     need_space( context.m_skip_nodes,     n_codes * 2 );
<a name="l00311"></a>00311 
<a name="l00312"></a>00312     <a class="code" href="structnih_1_1cuda_1_1_bintree__gen__context_1_1_split__task.html">Bintree_gen_context::Split_task</a>* task_queues[2] = {
<a name="l00313"></a>00313         thrust::raw_pointer_cast( &amp;(context.m_task_queues[0]).front() ),
<a name="l00314"></a>00314         thrust::raw_pointer_cast( &amp;(context.m_task_queues[1]).front() )
<a name="l00315"></a>00315     };
<a name="l00316"></a>00316     uint32* skip_nodes[2] = {
<a name="l00317"></a>00317         thrust::raw_pointer_cast( &amp;(context.m_skip_nodes).front() ),
<a name="l00318"></a>00318         thrust::raw_pointer_cast( &amp;(context.m_skip_nodes).front() + n_codes )
<a name="l00319"></a>00319     };
<a name="l00320"></a>00320 
<a name="l00321"></a>00321     uint32 in_queue  = 0;
<a name="l00322"></a>00322     uint32 out_queue = 1;
<a name="l00323"></a>00323 
<a name="l00324"></a>00324     context.m_counters.resize( 4 );
<a name="l00325"></a>00325     context.m_counters[ in_queue ]  = 1;
<a name="l00326"></a>00326     context.m_counters[ out_queue ] = 0;
<a name="l00327"></a>00327     context.m_counters[ 2 ]         = 0; <span class="comment">// leaf counter</span>
<a name="l00328"></a>00328 
<a name="l00329"></a>00329     context.m_task_queues[ in_queue ][0] = <a class="code" href="structnih_1_1cuda_1_1_bintree__gen__context_1_1_split__task.html">Bintree_gen_context::Split_task</a>( 0, 0, n_codes, bits-1 );
<a name="l00330"></a>00330     context.m_skip_nodes[0]              = uint32(-1);
<a name="l00331"></a>00331 
<a name="l00332"></a>00332     uint32 n_nodes = 1;
<a name="l00333"></a>00333 
<a name="l00334"></a>00334     <span class="comment">// start splitting from the most significant bit</span>
<a name="l00335"></a>00335     int32 level = bits-1;
<a name="l00336"></a>00336 
<a name="l00337"></a>00337     context.m_levels[ bits ] = 0;
<a name="l00338"></a>00338 
<a name="l00339"></a>00339     <span class="comment">// loop until there&#39;s tasks left in the input queue</span>
<a name="l00340"></a>00340     <span class="keywordflow">while</span> (context.m_counters[ in_queue ] &amp;&amp; level &gt;= 0)
<a name="l00341"></a>00341     {
<a name="l00342"></a>00342         context.m_levels[ level ] = n_nodes;
<a name="l00343"></a>00343 
<a name="l00344"></a>00344         tree.reserve_nodes( n_nodes + context.m_counters[ in_queue ]*2 );
<a name="l00345"></a>00345 
<a name="l00346"></a>00346         <span class="comment">// clear the output queue</span>
<a name="l00347"></a>00347         context.m_counters[ out_queue ] = 0;
<a name="l00348"></a>00348         cudaThreadSynchronize();
<a name="l00349"></a>00349 
<a name="l00350"></a>00350         bintree::split(
<a name="l00351"></a>00351             tree.get_context(),
<a name="l00352"></a>00352             max_leaf_size,
<a name="l00353"></a>00353             keep_singletons,
<a name="l00354"></a>00354             codes,
<a name="l00355"></a>00355             context.m_counters[ in_queue ],
<a name="l00356"></a>00356             task_queues[ in_queue ],
<a name="l00357"></a>00357             skip_nodes[ in_queue ],
<a name="l00358"></a>00358             thrust::raw_pointer_cast( &amp;context.m_counters.front() ) + out_queue,
<a name="l00359"></a>00359             task_queues[ out_queue ],
<a name="l00360"></a>00360             skip_nodes[ out_queue ],
<a name="l00361"></a>00361             n_nodes,
<a name="l00362"></a>00362             thrust::raw_pointer_cast( &amp;context.m_counters.front() ) + 2 );
<a name="l00363"></a>00363 
<a name="l00364"></a>00364         <span class="keyword">const</span> uint32 out_count = context.m_counters[ out_queue ];
<a name="l00365"></a>00365 
<a name="l00366"></a>00366         <span class="comment">// update the number of nodes</span>
<a name="l00367"></a>00367         n_nodes += out_count;
<a name="l00368"></a>00368 
<a name="l00369"></a>00369         <span class="comment">// swap the input and output queues</span>
<a name="l00370"></a>00370         std::swap( in_queue, out_queue );
<a name="l00371"></a>00371 
<a name="l00372"></a>00372         <span class="comment">// decrease the level</span>
<a name="l00373"></a>00373         --level;
<a name="l00374"></a>00374     }
<a name="l00375"></a>00375 
<a name="l00376"></a>00376     <span class="keywordflow">for</span> (; level &gt;= 0; --level)
<a name="l00377"></a>00377         context.m_levels[ level ] = n_nodes;
<a name="l00378"></a>00378 
<a name="l00379"></a>00379     <span class="comment">// generate a leaf for each of the remaining tasks</span>
<a name="l00380"></a>00380     if (context.m_counters[ in_queue ])
<a name="l00381"></a>00381     {
<a name="l00382"></a>00382         bintree::gen_leaves(
<a name="l00383"></a>00383             tree.get_context(),
<a name="l00384"></a>00384             context.m_counters[ in_queue ],
<a name="l00385"></a>00385             task_queues[ in_queue ],
<a name="l00386"></a>00386             skip_nodes[ in_queue ],
<a name="l00387"></a>00387             thrust::raw_pointer_cast( &amp;context.m_counters.front() ) + 2 );
<a name="l00388"></a>00388     }
<a name="l00389"></a>00389     context.m_nodes  = n_nodes;
<a name="l00390"></a>00390     context.m_leaves = context.m_counters[2];
<a name="l00391"></a>00391 }
<a name="l00392"></a>00392 
<a name="l00393"></a>00393 } <span class="comment">// namespace cuda</span>
<a name="l00394"></a>00394 } <span class="comment">// namespace nih</span>
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